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Prediction uncertainty estimation has clinical significance as it canpotentially quantify prediction reliability. Clinicians may trust 'blackbox'models more if robust reliability information is available, which may lead tomore models being adopted into clinical practice. There are several deeplearning-inspired uncertainty estimation techniques, but few are implemented onmedical datasets -- fewer on single institutional datasets/models. We sought tocompare dropout variational inference (DO), test-time augmentation (TTA),conformal predictions, and single deterministic methods for estimatinguncertainty using our model trained to predict feeding tube placement for 271head and neck cancer patients treated with radiation. We compared the areaunder the curve (AUC), sensitivity, specificity, positive predictive value(PPV), and negative predictive value (NPV) trends for each method at variouscutoffs that sought to stratify patients into 'certain' and 'uncertain'cohorts. These cutoffs were obtained by calculating the percentile"uncertainty" within the validation cohort and applied to the testing cohort.Broadly, the AUC, sensitivity, and NPV increased as the predictions were more'certain' -- i.e., lower uncertainty estimates. However, when a majority vote(implementing 2/3 criteria: DO, TTA, conformal predictions) or a stricterapproach (3/3 criteria) were used, AUC, sensitivity, and NPV improved without anotable loss in specificity or PPV. Especially for smaller, singleinstitutional datasets, it may be important to evaluate multiple estimationstechniques before incorporating a model into clinical practice.

Uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician's perspective